Buzzword or Benefit: How Real is Your DSP’s Machine Learning?

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Jay serves as SVP of Advertiser Platforms for Oath, overseeing sales and service teams across the demand platforms including video, mobile, display and programmatic TV. In his previous role, Jay was Regional VP of advertising sales teams for AOL’s Central Region, representing the AOL-Verizon portfolio of content properties and platforms. Prior to that, Jay oversaw Microsoft’s US Targeting & Exchange teams, leading both the Audience Targeting specialist teams and Microsoft’s programmatic sales efforts. Jay has also held positions at Vibrant Media, where he managed sales operations and evangelized the contextual space, and IDG, a premium trade publisher, selling branded advertising to leading technology agencies and marketers.

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Lots of DSPs are talking about using AI but how can brands tell what’s real? With that in mind, Jay Seideman, SVP at Oath Advertiser Platforms, discusses how brands can evaluate so-called AI technology in DSPs.

Right now, every major DSP is talking about machine learning. In an industry that’s big on buzzwords, it just might be the buzziest, and for good reason.

Today, where transparency and performance are all-important, marketers are required to be “more.” More efficient. More effective. More innovative.

So, marketers are looking at machine learning as a way to make smarter buys and exceed campaign expectations. Machine learning can dynamically deliver relevant brand messages to the right people, in the right context and at the right moment.

As a result, DSPs are raising it in every conversation. They know machine learning’s value and are highlighting it. But how should marketers respond? How can they determine what’s real and what’s PR?

Here are four key questions to consider when evaluating how a DSP is using machine learning.

How accurate and diverse are the data points?

In a DSP, machine learning effectiveness is all about the data. Highly accurate data points and a diversity of data sources — whether it be email, search, apps, user registration, content consumption, and more — are what make the engine run. And it shouldn’t come only from audience data, as is common in the RTB world. Instead, a DSP should also leverage deep-site segmentation data from both supply and demand. Only with a large amount of accurate and diverse data can a DSP create the best view of an advertiser’s most relevant audiences and reach them.

Fortunately, in the last year, data accuracy and diversity questions have become focus areas for marketers. Eighty-four percent of marketers say data accuracy is a critical concern, according to a recent Lotame study.

Where and how is machine learning used?

In assessing DSPs and their use of machine learning, it’s important to understand the areas and functions where it’s deployed. Every DSP does things differently. One might use machine learning in campaign optimization and forecasting. Others might use it in their modeling of predictive audiences, where deep learning using neural networks analyzes and scores relevant data sets to predict an audiences probability to perform a specific action. And others do both. Foundational machine learning use cases in a DSP can include:

Given its complexity, it’s important for marketers to understand how machine learning is activated across a DSP. By demystifying use cases and gaining clarity, they can make smarter decisions and more targeted plans.

How flexible is the system?

How flexible are the DSP’s machine learning capabilities? Flexibility is key because it speaks to the quality of the technology. For example, can the system optimize bidding for both first-price and second-price auction dynamics? Keep in mind, bidding for first-price inventory demands flexibility. It requires sophisticated prediction and forecasting of competing bids. Also, can the machine learning system optimize to to brand, performance and multi-level goals? The rubric here can vary dramatically. For these reasons, a DSP with malleable machine learning capabilities is increasingly important today.

Is everything working together?

It’s not enough for a DSP to feature the right algorithms. It needs the right algorithms that are working together. There are standard machine learning algorithms out there which any DSPs can leverage, but what really makes a machine learning engine stand apart is the ability for it to work in concert with other customized proprietary algorithms. This enables it to determine the best strategy and optimal bidding tactics to deliver against campaign goals. There must be connective tissue among systems so they can collaborate, learn from a campaign and create better performance. Believe it or not, many DSPs fail to deliver here.

Every DSP features machine learning technology today. However, each one has different capabilities and degrees of sophistication. For advertisers to understand the best tools for their purposes, they need to ask questions. These four are a good place to start and will help them move beyond the buzzwords.